Immune Checkpoint

Immune checkpoint inhibitors (ICIs) represent a revolutionary cancer therapy, but their effectiveness varies widely among patients. Current research focuses on improving prediction of ICI response by analyzing the complex interplay between tumor antigens, immune cells (like T cells), and immune checkpoint proteins (e.g., PD-1, PD-L1, ICOS, LAG3) within the tumor microenvironment. This involves developing sophisticated computational models, including transformer networks and deep learning algorithms, to analyze large datasets of clinical notes, genomic data, and high-resolution images from immunohistochemistry. These advancements aim to personalize cancer treatment, optimizing ICI use and improving patient outcomes by identifying those most likely to benefit.